Stock Market Analysis Using Python
Divya yadav, Dr. N. Thillaiarasu
STOCK Marketing performance measurement
In the global financial market trading is one of the most important tasks. The stock market forecast is an act of trying to determine the future value of another financial instrument traded on a currency exchange. This paper describes stock forecasts using Machine Learning. Technical and critical or seasonal analysis is widely used by stock marketers while making stock forecasts. Structured language is used to predict the stock market using machine learning by Python. In this paper we propose a Machine Learning (ML) method that will be trained from available stock data and gain intelligence and apply the knowledge gained from accurate predictions. In this context this study uses a Support Vector Machine (SVM) method to forecast the prices of capital and small capital in three different markets, using daily and daily price levels. Modeling and predicting the financial market has become an attractive topic for scholars and researchers from different fields of education. The financial market is an abstract concept in which financial assets such as stocks, bonds, and precious metal transactions occur between buyers and sellers. In the current state of the financial market, especially in the stock market, the prediction of the practice or price of stocks using machine learning techniques and neural networks is made a very interesting issue to be investigated. As Giles explained, financial forecasting is an example of the problem of hard-hitting signal due to loud, small noise sample size, non-stop, and inconsistent. Noisy features denote the incomplete information gap between the trading price of the previous stock and the volume on the future price. The stock market is sensitive to the political and macroeconomic environment. However, these two types of information are very complex and do not depend on each other. The above information that is not included in the features is considered to be audio. The sample size of financial data is determined by real-estate sales records. On the other hand, larger sample size means longer term records; on the other hand, the large sample size increases the uncertainty of the financial environment during the 2-sample period. In Burton's hypothesis, he suggests that predicting
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Unique Paper ID: 150009

Publication Volume & Issue: Volume 7, Issue 2

Page(s): 147 - 150
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